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| Titel |
Extreme events in total ozone over Arosa – Part 1: Application of extreme value theory |
| VerfasserIn |
H. E. Rieder, J. Staehelin, J. A. Maeder, T. Peter, M. Ribatet, A. C. Davison, R. Stübi, P. Weihs, F. Holawe |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 10, no. 20 ; Nr. 10, no. 20 (2010-10-25), S.10021-10031 |
| Datensatznummer |
250008851
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| Publikation (Nr.) |
 copernicus.org/acp-10-10021-2010.pdf |
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| Zusammenfassung |
| In this study ideas from extreme value theory are for the first time applied
in the field of stratospheric ozone research, because statistical analysis
showed that previously used concepts assuming a Gaussian distribution (e.g.
fixed deviations from mean values) of total ozone data do not adequately
address the structure of the extremes. We show that statistical extreme
value methods are appropriate to identify ozone extremes and to describe the
tails of the Arosa (Switzerland) total ozone time series. In order to
accommodate the seasonal cycle in total ozone, a daily moving threshold was
determined and used, with tools from extreme value theory, to analyse the
frequency of days with extreme low (termed ELOs) and high (termed EHOs)
total ozone at Arosa. The analysis shows that the Generalized Pareto
Distribution (GPD) provides an appropriate model for the frequency
distribution of total ozone above or below a mathematically well-defined
threshold, thus providing a statistical description of ELOs and EHOs. The
results show an increase in ELOs and a decrease in EHOs during the last
decades. The fitted model represents the tails of the total ozone data set
with high accuracy over the entire range (including absolute monthly minima
and maxima), and enables a precise computation of the frequency distribution
of ozone mini-holes (using constant thresholds). Analyzing the tails instead
of a small fraction of days below constant thresholds provides deeper
insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and
major volcanic eruptions, can be identified in the observed frequency of
extreme events throughout the time series. Overall the new approach to
analysis of extremes provides more information on time series properties and
variability than previous approaches that use only monthly averages and/or
mini-holes and mini-highs. |
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